Transcript GLOBAL OBSERVING SYSTEMS Global Climate Observing System GCOS Global Ocean Observing System GOOS Global Terrestrial Observing System GTOS.

GLOBAL OBSERVING SYSTEMS
Global Climate Observing System
GCOS
Global Ocean Observing System
GOOS
Global Terrestrial Observing System GTOS
GLOBAL TERRESTRIAL OBSERVING SYSTEM
Created to provide policy makers, resource
managers and researchers with access to the
data needed to detect, quantify, locate,
understand and warn of changes (especially
reductions) in the capacity of terrestrial
ecosystems to support sustainable development.
GT-NET Demonstration Project
 It will demonstrate the benefits of linking existing
networks by undertaking projects which generate
products which address global change issues.
 Demonstration projects will serve as test beds for
building collaboration and sharing experience among
networks and sites, including data sharing and exchange.
GT-NET key activities
 Define a clear policy on data and information access.
 Share and exchange environmental data, and
harmonize measurement methods.
 Develop standards for metadata as well as
local/regional/global in situ data sets.
 Undertake demonstration projects, the initial one to
estimate global net primary productivity of terrestrial
ecosystems.
GT-NET: Terms of reference
Develop a GT-NET documentation and information center. It will serve as
repository and dissemination point for data, policies on data management, and
dissemination and documentation of methods among GT-Net members.
 Maintain a global meta-database (TEMS) of networks and sites participating in GT- NET
and make it accessible on the web.
 Develop a personnel database and managed e-mail server maintained via the web.
 Use accepted GTOS policies, for the release and exchange of meta-data.
Implement a GT-NET demonstration project. This will provide global products
from satellite sensors that have been validated by GT-NET sites. Users will be
Climate Change scientific and technical advisory body, GT-Net sites and the
MODIS team of NASA.
 Products include :Landcover, snowcover, leaf area index (LAI), and net primary
productivity (NPP) - in a format suitable for participating sites.
 Similar validation data or basic climatological information, from the participating
sites to the GT-Net documentation and information centre.
GT-NET: participating networks
• Arab Centre for the Studies of Arid Zones and Dry Lands (ACSAD)
 Arctic Monitoring and Assessment Programme (AMAP)
 Chinese Ecosystem Research Network (CERN)
 Consultative Group on International Agricultural Research (CGIAR)
 Fluxnet (EuroFlux, Ameriflux etc.)
 International Cooperative Programme on Integrated
Monitoring of Air Pollution Effects on Ecosystem (ICP IM)
 Organismo Autonomo Parques Nationales
 Réseau d’Observatoires de Surveillance Ecologique à Long Terme (ROSELT)
 UK Environmental Change Network (ECN)
 US Long-term Ecological Research Networks (LTER)
 Worldwide Network of Biosphere Reserves (MAB-BR)
An example of global
Landcover (LC), Leaf Area
Index (LAI) and Net Primary
Production (NPP) terrestrial
variables that will be
produced from the Earth
Observing System (EOS)
every 8 days at 1 km.
An example of global Landcover that will be produced from the Earth
Observing System (EOS) every 8 days at 1 km.
These data will be valuable for ecological research and for land
management analysis, but first need field validation. (see Running et
al., 1994, and Justice et al., 1998 for details).
An example of global Leaf Area Index (LAI) that will be produced
from the Earth Observing System (EOS) every 8 days at 1 km.
These data will be valuable for ecological research and for land
management analysis, but first need field validation. (see Running et
al., 1994, and Justice et al., 1998 for details).
An example of global Net Primary Production (NPP) that will be
produced from the Earth Observing System (EOS) every 8 days at 1
km.
These data will be valuable for ecological research and for land
management analysis, but first need field validation. (see Running et
al., 1994, and Justice et al., 1998 for details).
Sites contributing to multiple programs have the highest synergy and efficiency. The programs depicted are:
a. GPPDI =
Global Primary Production Data Initiative,
b. FLUXNET = global network of eddy covariance flux towers,
c. Atm FLASK = global network of atmospheric flask sampling,
d. GTOS-NPP = special project to measure Net Primary Productivity in field sites worldwide,
e. BIGFOOT = study to establish scaling principles for sampling vegetation over large areas,
f. EOS-MODIS = Moderate Resolution Imaging Spectroradiometer on the Earth Observing System, the primary
terrestrial observation sensor,
g. VEMAP =
Vegetation/ Ecosystem Modeling and Analysis Project,
h. GAIM-NPP = IGBP project in Global Analysis Integration and Modeling study of global NPP.
Diagram of the
distribution of 1degree global
vegetation cells
related to precipitation
and temperature,
illustrating the
climatic distribution of
sites for complete
biome sampling (from
Churkina and Running,
1998). The FLUXNET
“A Global Terrestrial Monitoring Network Scaling Tower Fluxes with
Ecosystem Modeling and EOS Satellite Data” - S.W. Running, D.
Baldocchi, W. Cohen, S.T. Gower, D. Turner, P. Bakwin, K. Hibbard
sites are
superimposed.
A generalized FLUXNET tower configuration diagram, showing key carbon and water
fluxes measured. Atmospheric optical measurements, automated surface spectral
measurements, flask sampling and stable isotope sampling can be accommodated in
this framework.
Multi-year trends in monthly atmospheric CO2 measurements for two tall towers in
contrasting climates (NOAA/CMDL flask monitoring network). Note the differential
activity of CO2 within the forest canopies at 30-50 m height dominated by biological
dynamics compared to the mid-planetary boundary layer at 400-500 m where
atmospheric transport dominates. When coupled with atmospheric transport models,
these data can be used to estimate CO2 fluxes at regional scales (Bakwin et al., 1998).
Illustration of the three spatial scales that must be considered for ecological scaling and validation.
(1) Measures of vegetation parameters in the atmospheric footprint of the FLUXNET towers are
required for Soil-Vegetation-Atmosphere-Transfer models to simulate the NEP measured by the
towers. (2) A larger area of minimum 3x3 km must be sampled to provide ground truth of MODIS
LAI and NPP vegetation products. (3) The representativeness of the FLUXNET tower and MODIS
sampling site to the larger biome/climate complex must be evaluated by cross biome sampling.
(4)After these measurement scales are co-validated, synthesis of ground data, ecosystem models
and satellite data can be accomplished.
A general evaluation of the varying time scales and mechanistic complexity inherent in
various current Soil-Vegetation-Atmosphere-Transfer (SVAT) models. The MODIS
global NPP estimate is represented by the e, a model of minimum process complexity.
Models of higher process detail are required to validate and interpret the e models, but
cannot be run globally because of lack of data and computing limitations (from
Landsberg and Gower, 1997).
Top figure is an example of FLUXNET carbon balance data, weekly net
ecosystem exchange (NEE = NEP) measured by an eddy covariance fluxtower
for a temperate deciduous forest. Bottom figure is a comparison of SVAT
model simulation of NEE to observed NEE in the top figure
(Baldocchi,unpublished).
An example of ecosystem fluxes at regional scales. Daily GPP and transpiration for a
1000km2 mountain region in the USA. These ecosystem flux products were generated
by scaling stand parameters with satellite data, topography, soils, and microclimate
data, integrated with an ecosystem model (Running). Model extrapolation of fluxes
into complex topography is essential because fluxtowers are theoretically limited to
flat terrain (from White et al., 1998).
Critical vegetation variables
of LC, LAI and NPP are
measured at local and
regional scales, and used to
validate the global satellite
based estimates. NEP
measurements provide a
separate validation and
translation of the carbon
budget based NPP to
estimate commodity yields
(with local weather data if
available).